With the development of the biological information technologies, more and more wearable healthy and medical products can provide personalized healthy and medical services to the people by collecting and analyzing the human electrocardiogram signals, so that a person can acquire his healthy condition without going to any professional medical institutions.
Therefore, the automatic analysis of the electrocardiogram data gradually becomes a research hotspot in the biomedicine field at present. Most of the electrocardiogram data automatic analysis technologies currently are employed for the electrocardiogram data collected in a basic unit of hospital, thus the scale of the electrocardiogram data is very limited. But the service objects of the family health cloud platform are home users in the small, medium and even big cities, and everyday thousands of users upload long-term and short-term electrocardiogram data.
Currently, the health cloud platform adopts a serial electrocardiogram data analysis algorithm, which realizes real-time analysis and real-time feedback of the short-term electrocardiogram data, but the analysis of the long-term electrocardiogram data still costs much time and seriously affects the user's experience. For example, in the current family health cloud platform, the average response time from the upload to the feedback result analysis of the 24 h long-term electrocardiogram data is 35 s, and the consumed time is long.
Related researchers at home and abroad are actively attempting to promote the analysis and processing of the electrocardiogram data from various angles. Although many meaningful research achievements are made in the aspect of electrocardiogram data parallel processing, those research achievements just propose coarse-grained processing procedures for the electrocardiogram data analysis, while the problems occurred at present are still difficult to be solved.